Targeting the C-type Lectins-Mediated Host-Pathogen Interactions with Dextran
Bibliographic record
Abstract
Dextran, the α-1,6-linked glucose polymer widely used in biology and medicine, promises new applications. Linear dextran applied as a blood plasma substitute demonstrates a high rate of biocompatibility. Dextran is present in foods, drugs, and vaccines and in most cases is applied as a biologically inert substance. In this review we analyze dextran's cellular uptake principles, receptor specificity and, therefore, its ability to interfere with pathogen-lectin interactions: a promising basis for new antimicrobial strategies. Dextran-binding receptors in humans include the DC-SIGN (dendritic cell-specific intercellular adhesion molecule 3-grabbing nonintegrin) family receptors: DC-SIGN (CD209) and L-SIGN (the liver and lymphatic endothelium homologue of DC-SIGN), the mannose receptor (CD206), and langerin. These receptors take part in the uptake of pathogens by dendritic cells and macrophages and may also participate in the modulation of immune responses, mostly shown to be beneficial for pathogens per se rather than host(s). It is logical to predict that owing to receptor-specific interactions, dextran or its derivatives can interfere with these immune responses and improve infection outcome. Recent data support this hypothesis. We consider dextran a promising molecule for the development of lectin-glycan interaction-blocking molecules (such as DC-SIGN inhibitors) that could be applied in the treatment of diseases including tuberculosis, influenza, hepatitis B and C, human immunodeficiency virus infection and AIDS, etc. Dextran derivatives indeed change the pathology of infections dependent on DC-SIGN and mannose receptors. Complete knowledge of specific dextran-lectin interactions may also be important for development of future dextran applications in biological research and medicine.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".